25:["$","title",null,{"children":"ICM vs LTM | Chathura Jayalath | Observable"}] 49:T8f3,{ let measure = "S1" let trace_line_list = [] let annot_list = [] let tick_text_list = [] let disp = [] let d3colors = Plotly.d3.scale.category10(); let modelIdx = 0; let startIdx = 0; for (const modelName in data) { let model_measure = data[modelName][measure] let values = getAttributeData(model_measure, measure) let vsum = d3.sum(values) values = values.map(x => 100 * Math.abs(x) / vsum) let valueconfs = getAttributeData(model_measure, measure + "_conf") valueconfs = valueconfs.map(x => 100 * Math.abs(x)) let labels = getAttributeData(model_measure, "") let line_obj = ({ x:Array(labels.length).fill(0).map((x,i) => i + startIdx), y: values, error_y: { type: 'data', array: valueconfs, arrayminus: valueconfs }, type: 'scatter', mode: 'markers', marker: {size:10, color: rstudioColors[modelIdx]}, name: 'RRM ST' }) let annot_obj = ({ xref: 'x', yref: 'paper', x: (startIdx + labels.length - 1.0) / 2.0, xanchor: 'center', y: 1, yanchor: 'bottom', text: measure + ' ' + modelName, showarrow: false, font: { //family: 'sans serif', size: 18, color: rstudioColors[modelIdx] } }) trace_line_list.push(line_obj); annot_list.push(annot_obj); labels.forEach(e => tick_text_list.push(e)); disp.push(values); startIdx += labels.length; modelIdx += 1; } let layout = { height: 300, width: 1000, grid: {columns:1, rows:1, subplots:[['xy']]}, title: measure + ' of Models', showlegend:false, yaxis : { title: { text:'Percentage of \u03D5_F
Variance Explained', standoff:10}, //range: [-0.2,1], mode: 'array', tickvals: Array(51).fill(0).map((x,i) => i * 20), ticktext: Array(51).fill(0).map((x,i) => (i * 20) + '%') }, xaxis : { zeroline : false, tickvals: [0,1,2,4,5,6,8,9,10,12,13,14], ticktext: tick_text_list.map(x => var_to_symb[x]), tickfont:{ size : 16 }, tickangle:20 }, annotations: annot_list }; const div = DOM.element('div') Plotly.newPlot(div,trace_line_list,layout) return div }4a:Tb9b,{ let d3colors = Plotly.d3.scale.category10(); let trace_BRRM_ST = { x:[0,1,2], y: RRM_ST.map(x => x * 100 / d3.sum(RRM_ST)), error_y: { type: 'data', array: RRM_ST_conf.map(x => x * 100), arrayminus: RRM_ST_conf.map(x => x * 100)//.map((x,i) => d3.min([data[i],x]) ) }, type: 'scatter', mode: 'markers', marker: {size:10, color: rstudioColors[0]}, name: 'RRM ST' } let trace_BRRM_ST_text = { x:[-.15,0.85,1.85], //x:[11.85,12.85,13.85], y: RRM_ST.map(x => (x + 0.02) * 100 / d3.sum(RRM_ST)), text: RRM_ST.map(x => (x * 100 / d3.sum(RRM_ST)).toPrecision(3) + "%"), type: 'scatter', mode: 'text', name: 'BRRM ST Text' } let layout = { height: 300, width: 1000, grid: {columns:1, rows:1, subplots:[['xy']]}, //title:'ST of Models', showlegend:false, yaxis : { title: { text:'Percentage of \u03D5_F
Variance Explained', standoff:10}, //range: [-0.2,1], mode: 'array', tickvals: Array(51).fill(0).map((x,i) => i * 2), ticktext: Array(51).fill(0).map((x,i) => (i * 2) + '%') }, yaxis2 : { title: { text:'Percentage of \u03D5_F
Variance Explained', standoff:10}, //range: [-0.2,1], mode: 'array', tickvals: Array(51).fill(0).map((x,i) => i * 2), ticktext: Array(51).fill(0).map((x,i) => (i * 2) + '%'), overlaping:'y' }, xaxis : { zeroline : false, tickvals: [0,1,2,4,5,6,8,9,10,12,13,14], ticktext: ['\u03D5\u2080','\u03B2','q_b','\u03D5\u2080','\u03B2','q', '\u03D5\u2080','\u03B2','\u03B8','\u03D5\u2080','\u03B2','\u03C8'], tickfont:{ size : 16 }, tickangle:20 }, annotations: [{ xref: 'x', yref: 'paper', x: 1, xanchor: 'center', y: 1, yanchor: 'bottom', text: 'ST BRRM', showarrow: false, font: { //family: 'sans serif', size: 18, color: rstudioColors[0] } },{ xref: 'x', yref: 'paper', x: 5, xanchor: 'center', y: 1, yanchor: 'bottom', text: 'ST ICM', showarrow: false, font: { //family: 'sans serif', size: 18, color: rstudioColors[1] } },{ xref: 'x', yref: 'paper', x: 9, xanchor: 'center', y: 1, yanchor: 'bottom', text: 'ST LFTM', showarrow: false, font: { //family: 'sans serif', size: 18, color: rstudioColors[2] } },{ xref: 'x', yref: 'paper', x: 13, xanchor: 'center', y: 1, yanchor: 'bottom', text: 'ST LATM', showarrow: false, font: { //family: 'sans serif', size: 18, color: rstudioColors[3] } }] }; const div = DOM.element('div') Plotly.newPlot(div,[trace_BRRM_ST,trace_BRRM_ST_text],layout) return div }4b:T1537,{ let d3colors = Plotly.d3.scale.category10(); let trace_BRRM_ST = { x:[0,1,2], y: RRM_ST.map(x => x * 100 / d3.sum(RRM_ST)), error_y: { type: 'data', array: RRM_ST_conf.map(x => x * 100), arrayminus: RRM_ST_conf.map(x => x * 100)//.map((x,i) => d3.min([data[i],x]) ) }, type: 'scatter', mode: 'markers', marker: {size:10, color: rstudioColors[0]}, name: 'RRM ST' } let trace_BRRM_ST_text = { x:[-.15,0.85,1.85], //x:[11.85,12.85,13.85], y: RRM_ST.map(x => (x + 0.02) * 100 / d3.sum(RRM_ST)), text: RRM_ST.map(x => (x * 100 / d3.sum(RRM_ST)).toPrecision(3) + "%"), type: 'scatter', mode: 'text', name: 'BRRM ST Text' } let trace_ICM_ST = { //domain: {column:0, row:0}, x:[4,5,6], //x: ['Initial-Infects','Beta','ICM-Q','Initial-Infects','Beta','LFTM-Threshold'], y: ICM_ST.map(x => x * 100 / d3.sum(ICM_ST)), error_y: { type: 'data', array: ICM_ST_conf.map(x => x * 100), arrayminus: ICM_ST_conf.map(x => x * 100)//.map((x,i) => d3.min([data[i],x]) ) }, type: 'scatter', mode: 'markers', marker: {size:10, color: rstudioColors[1]}, name: 'ICM ST' } let trace_ICM_ST_text = { //x:[-.15,0.85,1.85], x:[3.85,4.85,5.85], y: ICM_ST.map(x => (x + 0.02) * 100 / d3.sum(ICM_ST)), text: ICM_ST.map(x => (x * 100 / d3.sum(ICM_ST)).toPrecision(3) + "%"), type: 'scatter', mode: 'text', name: 'ICM ST Text' } let trace_LFTM_ST = { //domain: {column:0, row:0}, x:[8,9,10], //x: ['Initial-Infects','Beta','ICM-Q','Initial-Infects','Beta','LFTM-Threshold'], y: LFTM_ST.map(x => x * 100 / d3.sum(LFTM_ST)), error_y: { type: 'data', array: LFTM_ST_conf.map(x => x * 100), arrayminus: LFTM_ST_conf.map(x => x * 100)//.map((x,i) => d3.min([data[i],x]) ) }, type: 'scatter', mode: 'markers', marker: {size:10, color: rstudioColors[2]}, name: 'LFTM ST' } let trace_LFTM_ST_text = { //x:[3.85,4.85,5.85], x:[7.85,8.85,9.85], y: LFTM_ST.map(x => (x + 0.02) * 100 / d3.sum(LFTM_ST)), text: LFTM_ST.map(x => (x * 100 / d3.sum(LFTM_ST)).toPrecision(3) + "%"), type: 'scatter', mode: 'text', name: 'LFTM ST Text' } let trace_LATM_ST = { //domain: {column:0, row:0}, x:[12,13,14], //x: ['Initial-Infects','Beta','ICM-Q','Initial-Infects','Beta','LFTM-Threshold'], y: LTM_ST.map(x => x * 100 / d3.sum(LTM_ST)), error_y: { type: 'data', array: LTM_ST_conf.map(x => x * 100), arrayminus: LTM_ST_conf.map(x => x * 100)//.map((x,i) => d3.min([data[i],x]) ) }, type: 'scatter', mode: 'markers', marker: {size:10, color: rstudioColors[3]}, name: 'LATM ST' } let trace_LATM_ST_text = { //x:[7.85,8.85,9.85], x:[11.85,12.85,13.85], y: LTM_ST.map(x => (Math.abs(x) + 0.02) * 100 / d3.sum(LTM_ST)), text: LTM_ST.map(x => (Math.abs(x * 100) / d3.sum(LTM_ST)).toPrecision(3) + "%"), type: 'scatter', mode: 'text', name: 'LATM S1 Text' } let layout = { height: 300, width: 1000, grid: {columns:1, rows:1, subplots:[['xy']]}, //title:'ST of Models', showlegend:false, yaxis : { title: { text:'Percentage of \u03D5_F
Variance Explained', standoff:10}, //range: [-0.2,1], mode: 'array', tickvals: Array(51).fill(0).map((x,i) => i * 2), ticktext: Array(51).fill(0).map((x,i) => (i * 2) + '%') }, yaxis2 : { title: { text:'Percentage of \u03D5_F
Variance Explained', standoff:10}, //range: [-0.2,1], mode: 'array', tickvals: Array(51).fill(0).map((x,i) => i * 2), ticktext: Array(51).fill(0).map((x,i) => (i * 2) + '%'), overlaping:'y' }, xaxis : { zeroline : false, tickvals: [0,1,2,4,5,6,8,9,10,12,13,14], ticktext: ['\u03D5\u2080','\u03B2','q_b','\u03D5\u2080','\u03B2','q', '\u03D5\u2080','\u03B2','\u03B8','\u03D5\u2080','\u03B2','\u03C8'], tickfont:{ size : 16 }, tickangle:20 }, annotations: [{ xref: 'x', yref: 'paper', x: 1, xanchor: 'center', y: 1, yanchor: 'bottom', text: 'ST BRRM', showarrow: false, font: { //family: 'sans serif', size: 18, color: rstudioColors[0] } },{ xref: 'x', yref: 'paper', x: 5, xanchor: 'center', y: 1, yanchor: 'bottom', text: 'ST ICM', showarrow: false, font: { //family: 'sans serif', size: 18, color: rstudioColors[1] } },{ xref: 'x', yref: 'paper', x: 9, xanchor: 'center', y: 1, yanchor: 'bottom', text: 'ST LFTM', showarrow: false, font: { //family: 'sans serif', size: 18, color: rstudioColors[2] } },{ xref: 'x', yref: 'paper', x: 13, xanchor: 'center', y: 1, yanchor: 'bottom', text: 'ST LATM', showarrow: false, font: { //family: 'sans serif', size: 18, color: rstudioColors[3] } }] }; const div = DOM.element('div') Plotly.newPlot(div,[trace_BRRM_ST,trace_ICM_ST,trace_LFTM_ST, trace_LATM_ST,trace_BRRM_ST_text,trace_ICM_ST_text,trace_LATM_ST_text,trace_LFTM_ST_text],layout) return div }4c:T1476,{ let d3colors = Plotly.d3.scale.category10(); let trace_BRRM_S1 = { x:[0,1,2], y: RRM_S1.map(x => x * 100), error_y: { type: 'data', array: RRM_S1_conf.map(x => x * 100), arrayminus: RRM_S1_conf.map(x => x * 100)//.map((x,i) => d3.min([data[i],x]) ) }, type: 'scatter', mode: 'markers', marker: {size:10, color: rstudioColors[0]}, name: 'BRRM S1' } let trace_BRRM_S1_text = { x:[-.15,0.85,1.85], //x:[11.85,12.85,13.85], y: RRM_S1.map(x => (x + 0.01) * 100), text: RRM_S1.map(x => (x * 100).toPrecision(3) + "%"), type: 'scatter', mode: 'text', name: 'BRRM S1 Text' } let trace_ICM_S1 = { x:[4,5,6], //x: ['Initial-Infects','Beta','ICM-Q','Initial-Infects','Beta','LFTM-Threshold'], y: ICM_S1.map(x => x * 100), error_y: { type: 'data', array: ICM_S1_conf.map(x => x * 100), arrayminus: ICM_S1_conf.map(x => x * 100)//.map((x,i) => d3.min([data[i],x]) ) }, type: 'scatter', mode: 'markers', marker: {size:10, color: rstudioColors[1]}, name: 'ICM S1' } let trace_ICM_S1_text = { //x:[-.15,0.85,1.85], x:[3.85,4.85,5.85], y: ICM_S1.map(x => (x + 0.01) * 100), text: ICM_S1.map(x => (x * 100).toPrecision(3) + "%"), type: 'scatter', mode: 'text', name: 'ICM S1 Text' } let trace_LFTM_S1 = { //domain: {column:0, row:1}, //xaxis: 'x2', x:[8,9,10], //x: ['Initial-Infects','Beta','ICM-Q','Initial-Infects','Beta','LFTM-Threshold'], y: LFTM_S1.map(x => x * 100), error_y: { type: 'data', array: LFTM_S1_conf.map(x => x * 100), arrayminus: LFTM_S1_conf.map(x => x * 100)//.map((x,i) => d3.min([data[i],x]) ) }, type: 'scatter', mode: 'markers', marker: {size:10, color: rstudioColors[2]}, name: 'LFTM S1' } let trace_LFTM_S1_text = { //x:[3.85,4.85,5.85], x:[7.85,8.85,9.85], y: LFTM_S1.map(x => (x + 0.01) * 100), text: LFTM_S1.map(x => (x * 100).toPrecision(3) + "%"), type: 'scatter', mode: 'text', name: 'LFTM S1 Text' } let trace_LATM_S1 = { //domain: {column:0, row:1}, //xaxis: 'x2', x:[12,13,14], //x: ['Initial-Infects','Beta','ICM-Q','Initial-Infects','Beta','LFTM-Threshold'], y: LTM_S1.map(x => x * 100), error_y: { type: 'data', array: LTM_S1_conf.map(x => x * 100), arrayminus: LTM_S1_conf.map(x => x * 100)//.map((x,i) => d3.min([data[i],x]) ) }, type: 'scatter', mode: 'markers', marker: {size:10, color: rstudioColors[3]}, name: 'LATM S1' } let trace_LATM_S1_text = { //x:[7.85,8.85,9.85], x:[11.85,12.85,13.85], y: LTM_S1.map(x => (Math.abs(x) + 0.01) * 100), text: LTM_S1.map(x => (Math.abs(x * 100)).toPrecision(3) + "%"), type: 'scatter', mode: 'text', name: 'LATM S1 Text' } let layout = { height: 300, width: 1000, grid: {columns:1, rows:1, subplots:[['xy']]}, //title:'ST of Models', showlegend:false, yaxis : { title: { text:'Percentage of \u03D5_F
Variance Explained', standoff:10}, //range: [-0.2,1], mode: 'array', tickvals: Array(51).fill(0).map((x,i) => i * 2), ticktext: Array(51).fill(0).map((x,i) => (i * 2) + '%') }, yaxis2 : { title: { text:'Percentage of \u03D5_F
Variance Explained', standoff:10}, //range: [-0.2,1], mode: 'array', tickvals: Array(51).fill(0).map((x,i) => i * 2), ticktext: Array(51).fill(0).map((x,i) => (i * 2) + '%'), overlaping:'y' }, xaxis : { zeroline : false, tickvals: [0,1,2,4,5,6,8,9,10,12,13,14], ticktext: ['\u03D5\u2080','\u03B2','q_b','\u03D5\u2080','\u03B2','q', '\u03D5\u2080','\u03B2','\u03B8','\u03D5\u2080','\u03B2','\u03C8'], tickfont:{ size : 16 }, tickangle:20 }, annotations: [{ xref: 'x', yref: 'paper', x: 1, xanchor: 'center', y: 1, yanchor: 'bottom', text: 'S1 BRRM', showarrow: false, font: { //family: 'sans serif', size: 18, color: rstudioColors[0] } },{ xref: 'x', yref: 'paper', x: 5, xanchor: 'center', y: 1, yanchor: 'bottom', text: 'S1 ICM', showarrow: false, font: { //family: 'sans serif', size: 18, color: rstudioColors[1] } },{ xref: 'x', yref: 'paper', x: 9, xanchor: 'center', y: 1, yanchor: 'bottom', text: 'S1 LFTM', showarrow: false, font: { //family: 'sans serif', size: 18, color: rstudioColors[2] } },{ xref: 'x', yref: 'paper', x: 13, xanchor: 'center', y: 1, yanchor: 'bottom', text: 'S1 LATM', showarrow: false, font: { //family: 'sans serif', size: 18, color: rstudioColors[3] } }] }; const div = DOM.element('div') Plotly.newPlot(div,[trace_BRRM_S1, trace_ICM_S1, trace_LFTM_S1, trace_LATM_S1,trace_BRRM_S1_text,trace_ICM_S1_text,trace_LFTM_S1_text,trace_LATM_S1_text],layout) return div }4d:T1224,{ let d3colors = Plotly.d3.scale.category10(); let trace_BRRM_S2 = { x:[0,1,2], //x:[12,13,14], y: RRM_S2.map(x => x * 100), error_y: { type: 'data', array: RRM_S2_conf.map(x => x * 100), arrayminus: RRM_S2_conf.map(x => x * 100) }, type: 'scatter', mode: 'markers', marker: {size:10, color: rstudioColors[0]}, name: 'BRRM S2' } let trace_BRRM_S2_text = { x:[-.15,0.85,1.85], //x:[11.85,12.85,13.85], y: RRM_S2.map(x => (x + 0.01) * 100), text: RRM_S2.map(x => (x * 100).toPrecision(3) + "%"), type: 'scatter', mode: 'text', name: 'BRRM S2 Text' } let trace_ICM_S2 = { //x:[0,1,2], x:[4,5,6], y: ICM_S2.map(x => x * 100), error_y: { type: 'data', array: ICM_S2_conf.map(x => x * 100), arrayminus: ICM_S2_conf.map(x => x * 100) }, type: 'scatter', mode: 'markers', marker: {size:10, color: rstudioColors[1]}, name: 'ICM S2' } let trace_ICM_S2_text = { //x:[-.15,0.85,1.85], x:[3.85,4.85,5.85], y: ICM_S2.map(x => (x + 0.01) * 100), text: ICM_S2.map(x => (x * 100).toPrecision(3) + "%"), type: 'scatter', mode: 'text', name: 'ICM S2 Text' } let trace_LFTM_S2 = { //x:[4,5,6], x:[8,9,10], y: LFTM_S2.map(x => x * 100), error_y: { type: 'data', array: LFTM_S2_conf.map(x => x * 100), arrayminus: LFTM_S2_conf.map(x => x * 100) }, type: 'scatter', mode: 'markers+text', marker: {size:10, color: rstudioColors[2]}, name: 'LFTM S2' } let trace_LFTM_S2_text = { //x:[3.85,4.85,5.85], x:[7.85,8.85,9.85], y: LFTM_S2.map(x => (x + 0.01) * 100), text: LFTM_S2.map(x => (x * 100).toPrecision(3) + "%"), type: 'scatter', mode: 'text', name: 'LFTM S2 Text' } let trace_LATM_S2 = { //x:[8,9,10], x:[12,13,14], y: LTM_S2.map(x => Math.abs(x) * 100), error_y: { type: 'data', array: LTM_S2_conf.map(x => x * 100), arrayminus: LTM_S2_conf.map(x => x * 100) }, type: 'scatter', mode: 'markers', marker: {size:10, color: rstudioColors[3]}, name: 'LATM S2' } let trace_LATM_S2_text = { //x:[7.85,8.85,9.85], x:[11.85,12.85,13.85], y: LTM_S2.map(x => (Math.abs(x) + 0.01) * 100), text: LTM_S2.map(x => (Math.abs(x * 100)).toPrecision(3) + "%"), type: 'scatter', mode: 'text', name: 'LATM S2 Text' } let layout = { height: 300, width: 1000, grid: {columns:1, rows:1, subplots:[['xy']]}, //title:'ST of Models', showlegend:false, yaxis : { title: { text:'Percentage of \u03D5_F
Variance Explained', standoff:10, font:{ size: 14}}, //range: [-0.2,1], mode: 'array', tickvals: Array(51).fill(0).map((x,i) => i * 2), ticktext: Array(51).fill(0).map((x,i) => (i * 2) + '%') }, xaxis : { zeroline : false, tickvals: [0,1,2,4,5,6,8,9,10,12,13,14], ticktext: RRM_S2_names.concat(ICM_S2_names).concat(LFTM_S2_names).concat(LTM_S2_names), tickfont:{ size : 16 }, tickangle:20 //['Initial-Infects','Beta','ICM-Q','Initial-Infects','Beta','LFTM-Threshold','Initial-Infects','Beta','LTM-Threshold','Initial-Infects','Beta','RRM-Q'] }, annotations: [{ xref: 'x', yref: 'paper', x: 5, xanchor: 'center', y: 1, yanchor: 'bottom', text: 'S2 ICM', showarrow: false, font: { //family: 'sans serif', size: 18, color: rstudioColors[1] } },{ xref: 'x', yref: 'paper', x: 9, xanchor: 'center', y: 1, yanchor: 'bottom', text: 'S2 LFTM', showarrow: false, font: { //family: 'sans serif', size: 18, color: rstudioColors[2] //'#ff7f0e' } },{ xref: 'x', yref: 'paper', x: 13, xanchor: 'center', y: 1, yanchor: 'bottom', text: 'S2 LATM', showarrow: false, font: { //family: 'sans serif', size: 18, color: rstudioColors[3] } },{ xref: 'x', yref: 'paper', x: 1, xanchor: 'center', y: 1, yanchor: 'bottom', text: 'S2 BRRM', showarrow: false, font: { //family: 'sans serif', size: 18, color: rstudioColors[0] } }] }; const div = DOM.element('div') Plotly.newPlot(div,[trace_ICM_S2,trace_LFTM_S2, trace_LATM_S2, trace_BRRM_S2, trace_LFTM_S2_text, trace_ICM_S2_text, trace_BRRM_S2_text, trace_LATM_S2_text],layout) return div }4e:T1ac9,{ let d3colors = Plotly.d3.scale.category10(); let trace1 = { //domain: {column:0, row:0}, x:[0,1,2], //x: ['Initial-Infects','Beta','ICM-Q','Initial-Infects','Beta','LFTM-Threshold'], y: ICM_ST.map(x => x * 100 / d3.sum(ICM_ST)), error_y: { type: 'data', array: ICM_ST_conf.map(x => x * 100), arrayminus: ICM_ST_conf.map(x => x * 100)//.map((x,i) => d3.min([data[i],x]) ) }, type: 'scatter', mode: 'markers', marker: {size:10}, name: 'ICM ST' } let trace2 = { //domain: {column:0, row:0}, x:[4,5,6], //x: ['Initial-Infects','Beta','ICM-Q','Initial-Infects','Beta','LFTM-Threshold'], y: LFTM_ST.map(x => x * 100 / d3.sum(LFTM_ST)), error_y: { type: 'data', array: LFTM_ST_conf.map(x => x * 100), arrayminus: LFTM_ST_conf.map(x => x * 100)//.map((x,i) => d3.min([data[i],x]) ) }, type: 'scatter', mode: 'markers', marker: {size:10}, name: 'LFTM ST' } let trace3 = { //domain: {column:0, row:1}, yaxis: 'y2', //xaxis: 'x2', x:[0,1,2], //x: ['Initial-Infects','Beta','ICM-Q','Initial-Infects','Beta','LFTM-Threshold'], y: ICM_S1.map(x => x * 100), error_y: { type: 'data', array: ICM_S1_conf.map(x => x * 100), arrayminus: ICM_S1_conf.map(x => x * 100)//.map((x,i) => d3.min([data[i],x]) ) }, type: 'scatter', mode: 'markers', marker: {size:10}, name: 'ICM S1' } let trace4 = { //domain: {column:0, row:1}, yaxis: 'y2', //xaxis: 'x2', x:[4,5,6], //x: ['Initial-Infects','Beta','ICM-Q','Initial-Infects','Beta','LFTM-Threshold'], y: LFTM_S1.map(x => x * 100), error_y: { type: 'data', array: LFTM_S1_conf.map(x => x * 100), arrayminus: LFTM_S1_conf.map(x => x * 100)//.map((x,i) => d3.min([data[i],x]) ) }, type: 'scatter', mode: 'markers', marker: {size:10}, name: 'LFTM S1' } let trace5 = { //domain: {column:0, row:0}, x:[8,9,10], //x: ['Initial-Infects','Beta','ICM-Q','Initial-Infects','Beta','LFTM-Threshold'], y: LTM_ST.map(x => x * 100 / d3.sum(LTM_ST)), error_y: { type: 'data', array: LTM_ST_conf.map(x => x * 100), arrayminus: LTM_ST_conf.map(x => x * 100)//.map((x,i) => d3.min([data[i],x]) ) }, type: 'scatter', mode: 'markers', marker: {size:10}, name: 'LTM ST' } let trace6 = { //domain: {column:0, row:1}, yaxis: 'y2', //xaxis: 'x2', x:[8,9,10], //x: ['Initial-Infects','Beta','ICM-Q','Initial-Infects','Beta','LFTM-Threshold'], y: LTM_S1.map(x => x * 100), error_y: { type: 'data', array: LTM_S1_conf.map(x => x * 100), arrayminus: LTM_S1_conf.map(x => x * 100)//.map((x,i) => d3.min([data[i],x]) ) }, type: 'scatter', mode: 'markers', marker: {size:10}, name: 'LTM S1' } let trace7 = { x:[12,13,14], y: RRM_ST.map(x => x * 100 / d3.sum(RRM_ST)), error_y: { type: 'data', array: RRM_ST_conf.map(x => x * 100), arrayminus: RRM_ST_conf.map(x => x * 100)//.map((x,i) => d3.min([data[i],x]) ) }, type: 'scatter', mode: 'markers', marker: {size:10}, name: 'RRM ST' } let trace8 = { yaxis: 'y2', x:[12,13,14], y: RRM_S1.map(x => x * 100), error_y: { type: 'data', array: RRM_S1_conf.map(x => x * 100), arrayminus: RRM_S1_conf.map(x => x * 100)//.map((x,i) => d3.min([data[i],x]) ) }, type: 'scatter', mode: 'markers', marker: {size:10}, name: 'RRM S1' } let layout = { height: 500, width: 1000, grid: {columns:1, rows:2, subplots:[['xy'],['xy2']]}, //title:'ST of Models', showlegend:false, yaxis : { title: { text:'Percentage of \u03D5_F
Variance Explained', standoff:10}, //range: [-0.2,1], mode: 'array', tickvals: Array(51).fill(0).map((x,i) => i * 2), ticktext: Array(51).fill(0).map((x,i) => (i * 2) + '%') }, yaxis2 : { title: { text:'Percentage of \u03D5_F
Variance Explained', standoff:10}, //range: [-0.2,1], mode: 'array', tickvals: Array(51).fill(0).map((x,i) => i * 2), ticktext: Array(51).fill(0).map((x,i) => (i * 2) + '%'), overlaping:'y' }, xaxis : { zeroline: false, tickvals: [0,1,2,4,5,6,8,9,10,12,13,14], ticktext: ['Initial-Infects','Beta','ICM-Q','Initial-Infects','Beta','LFTM-Threshold','Initial-Infects','Beta','LTM-Threshold','Initial-Infects','Beta','RRM-Q'] }, annotations: [{ xref: 'x', yref: 'paper', x: 1, xanchor: 'center', y: 1, yanchor: 'bottom', text: 'ST ICM', showarrow: false, font: { //family: 'sans serif', size: 18, color: '#1f77b4' } },{ xref: 'x', yref: 'paper', x: 5, xanchor: 'center', y: 1, yanchor: 'bottom', text: 'ST LFTM', showarrow: false, font: { //family: 'sans serif', size: 18, color: '#ff7f0e' } },{ xref: 'x', yref: 'paper', x: 9, xanchor: 'center', y: 1, yanchor: 'bottom', text: 'ST LATM', showarrow: false, font: { //family: 'sans serif', size: 18, color: '#9467bd' } },{ xref: 'x', yref: 'paper', x: 13, xanchor: 'center', y: 1, yanchor: 'bottom', text: 'ST BRRM', showarrow: false, font: { //family: 'sans serif', size: 18, color: pdefColors[6] } },{ xref: 'x', yref: 'paper', x: 1, xanchor: 'center', y: 0.45, yanchor: 'bottom', text: 'S1 ICM', showarrow: false, font: { //family: 'sans serif', size: 18, color: '#2ca02c' } },{ xref: 'x', yref: 'paper', x: 5, xanchor: 'center', y: 0.45, yanchor: 'bottom', text: 'S1 LFTM', showarrow: false, font: { //family: 'sans serif', size: 18, color: '#d62728' } },{ xref: 'x', yref: 'paper', x: 9, xanchor: 'center', y: 0.45, yanchor: 'bottom', text: 'S1 LATM', showarrow: false, font: { //family: 'sans serif', size: 18, color: '#8c564b' } },{ xref: 'x', yref: 'paper', x: 13, xanchor: 'center', y: 0.45, yanchor: 'bottom', text: 'S1 BRRM', showarrow: false, font: { //family: 'sans serif', size: 18, color: pdefColors[7] } }] }; const div = DOM.element('div') Plotly.newPlot(div,[trace1,trace2,trace3,trace4,trace5,trace6,trace7,trace8],layout) return div }4f:T878,allData = ({ "LFTM" : ({ 'model': 'LFTM', 'Parameter': ['S1', 'S1_conf', 'ST', 'ST_conf'], 'initial-infects':[0.002632, 0.023207, 0.867161, 0.023907], 'beta': [0.020436, 0.025726, 0.875839, 0.025686], 'ltm-rel-threshold': [0.022768, 0.024580, 0.905579, 0.026081], 'S2_Parameters' : ['Parameter_1', 'Parameter_2', 'S2', 'S2_conf'], 'S2_Values': [ ['\u03D5\u2080', '\u03B2', 0.017900, 0.035860], ['\u03D5\u2080', '\u03B8', 0.046634, 0.033798], ['\u03B2', '\u03B8', 0.001285, 0.037534] ] }), 'LATM' : ({ 'model' : 'LATM', 'Parameter': ['S1', 'S1_conf', 'ST', 'ST_conf'], 'initial-infects' : [0.013153, 0.024578, 0.836812, 0.022517], 'beta' : [0.028004, 0.029337, 0.887586, 0.019873], 'ltm-threshold' : [0.046437, 0.028436, 0.905414, 0.024037], 'S2_Parameters' : ['Parameter_1', 'Parameter_2', 'S2', 'S2_conf'], 'S2_Values': [ ['\u03D5\u2080', '\u03B2', 0.016589, 0.035908], ['\u03D5\u2080', '\u03C8', 0.035354, 0.035541], ['\u03B2', '\u03C8', -0.007218, 0.037109] ] }), 'ICM' : ({ 'model' : 'ICM', 'Parameter': ['S1', 'S1_conf', 'ST', 'ST_conf'], 'initial-infects' : [0.015401, 0.025985, 0.843898, 0.023307], 'beta' : [0.016456, 0.025274, 0.874631, 0.023220], 'icm-q' : [0.024808, 0.027072, 0.895070, 0.025315], 'S2_Parameters': ['Parameter_1', 'Parameter_2', 'S2', 'S2_conf'], 'S2_Values': [ ['\u03D5\u2080', '\u03B2', 0.030180, 0.038046 ], ['\u03D5\u2080', 'q', 0.026902, 0.038978], ['\u03B2', 'q', 0.038491, 0.037101] ] }), 'RRM' : ({ 'model' : 'RRM', 'Parameter': ['S1', 'S1_conf', 'ST', 'ST_conf' ], 'initial-infects': [0.038709, 0.027743, 0.862372, 0.030649], 'beta': [0.022281, 0.029408, 0.890367, 0.031750], 'rrabm-q': [0.034587, 0.027922, 0.892149, 0.030486], 'S2_Parameters': ['Parameter_1', 'Parameter_2', 'S2', 'S2_conf'], 'S2_Values': [ ['\u03D5\u2080', '\u03B2', 0.015067, 0.036922], ['\u03D5\u2080', 'q_b', 0.008669, 0.040222], ['\u03B2', 'q_b', 0.006217, 0.035999] ] }) })50:Ta5b,{ let trace1 = { x:[0,1,2], //x: ['Initial-Infects','Beta','ICM-Q','Initial-Infects','Beta','LFTM-Threshold'], y: ICM_ST.map(x => x * 100 / d3.sum(ICM_ST)), error_y: { type: 'data', array: ICM_ST_conf.map(x => x * 100), arrayminus: ICM_ST_conf.map(x => x * 100)//.map((x,i) => d3.min([data[i],x]) ) }, type: 'scatter', mode: 'markers', marker: {size:10} } let trace2 = { x:[4,5,6], //x: ['Initial-Infects','Beta','ICM-Q','Initial-Infects','Beta','LFTM-Threshold'], y: LFTM_ST.map(x => x * 100 / d3.sum(LFTM_ST)), error_y: { type: 'data', array: LFTM_ST_conf.map(x => x * 100), arrayminus: LFTM_ST_conf.map(x => x * 100)//.map((x,i) => d3.min([data[i],x]) ) }, type: 'scatter', mode: 'markers', marker: {size:10} } let trace3 = { x:[8,9,10], //x: ['Initial-Infects','Beta','ICM-Q','Initial-Infects','Beta','LATM-Threshold'], y: LTM_ST.map(x => x * 100 / d3.sum(LTM_ST)), error_y: { type: 'data', array: LTM_ST_conf.map(x => x * 100), arrayminus: LTM_ST_conf.map(x => x * 100)//.map((x,i) => d3.min([data[i],x]) ) }, type: 'scatter', mode: 'markers', marker: {size:10} } let layout = { title:'ST of Models', showlegend:false, width: 1000, yaxis : { title: 'Percentage of Variance Explained', //range: [-0.2,1], mode: 'array', tickvals: Array(11).fill(0).map((x,i) => i * 10), ticktext: Array(11).fill(0).map((x,i) => (i * 10) + '%') }, xaxis : { tickvals: [0,1,2,4,5,6,8,9,10], ticktext: ['Initial-Infects','Beta','ICM-Q','Initial-Infects','Beta','LFTM-Threshold','Initial-Infects','Beta','LATM-Threshold'] }, annotations: [{ xref: 'x', yref: 'paper', x: 1, xanchor: 'center', y: 1, yanchor: 'bottom', text: 'ICM', showarrow: false, font: { //family: 'sans serif', size: 18, color: '#1f77b4' } },{ xref: 'x', yref: 'paper', x: 5, xanchor: 'center', y: 1, yanchor: 'bottom', text: 'LFTM', showarrow: false, font: { //family: 'sans serif', size: 18, color: '#ff7f0e' } },{ xref: 'x', yref: 'paper', x: 9, xanchor: 'center', y: 1, yanchor: 'bottom', text: 'LATM', showarrow: false, font: { //family: 'sans serif', size: 18, color: '#2ca02c' } }] }; const div = DOM.element('div') Plotly.newPlot(div,[trace1,trace2,trace3],layout) return div }51:T751,{ let trace1 = { x:[0,1,2], //x: ['Initial-Infects','Beta','ICM-Q','Initial-Infects','Beta','LFTM-Threshold'], y: ICM_S1.map(x => x * 100), error_y: { type: 'data', array: ICM_S1_conf.map(x => x * 100), arrayminus: ICM_S1_conf.map(x => x * 100)//.map((x,i) => d3.min([data[i],x]) ) }, type: 'scatter', mode: 'markers', marker: {size:10} } let trace2 = { x:[4,5,6], //x: ['Initial-Infects','Beta','ICM-Q','Initial-Infects','Beta','LFTM-Threshold'], y: LFTM_S1.map(x => x * 100), error_y: { type: 'data', array: LFTM_S1_conf.map(x => x * 100), arrayminus: LFTM_S1_conf.map(x => x * 100)//.map((x,i) => d3.min([data[i],x]) ) }, type: 'scatter', mode: 'markers', marker: {size:10} } let layout = { title:'S1 of Models', showlegend:false, yaxis : { title: 'Percentage of Variance Explained', //range: [-0.2,1], mode: 'array', tickvals: Array(11).fill(0).map((x,i) => i * 10), ticktext: Array(11).fill(0).map((x,i) => (i * 10) + '%') }, xaxis : { tickvals: [0,1,2,4,5,6], ticktext: ['Initial-Infects','Beta','ICM-Q','Initial-Infects','Beta','LFTM-Threshold'] }, annotations: [{ xref: 'x', yref: 'paper', x: 1, xanchor: 'center', y: 1, yanchor: 'bottom', text: 'ICM', showarrow: false, font: { //family: 'sans serif', size: 18, color: '#1f77b4' } },{ xref: 'x', yref: 'paper', x: 5, xanchor: 'center', y: 1, yanchor: 'bottom', text: 'LFTM', showarrow: false, font: { //family: 'sans serif', size: 18, color: '#ff7f0e' } }] }; const div = DOM.element('div') Plotly.newPlot(div,[trace1,trace2],layout) return div }52:Ta3c,{ let trace1 = { values: ICM_S1, labels: ['Initial-Infects', 'Beta', 'ICM-Q','ICM-Interactions'], type: 'pie', textinfo: 'label+percent', textposition: 'outside', domain: {column: 0, row:0}, name: 'ICM S1' } let trace2 = { values: ICM_ST, labels: ['Initial-Infects', 'Beta', 'ICM-Q'], type: 'pie', textinfo: 'label+percent', textposition: 'outside', domain: {column: 0, row:1}, name: 'ICM ST' } let trace3 = { values: LFTM_S1, labels: ['Initial-Infects', 'Beta', 'LFTM-Threshold','LFTM-Interactions'], type: 'pie', textinfo: 'label+percent', textposition: 'outside', domain: {column: 1, row:0}, name: 'LFTM S1' } let trace4 = { values: LFTM_ST, labels: ['Initial-Infects', 'Beta', 'LFTM-Threshold'], type: 'pie', textinfo: 'label+percent', textposition: 'outside', domain: {column: 1, row:1}, name: 'LFTM ST' } /*let trace5 = { values: ICM_S2, labels: ['Initial-Infects * Beta', 'Initial-Infects * ICM-Q', 'Beta * ICM-Q','Other'], type: 'pie', textinfo: 'label+percent', textposition: 'outside', domain: {column: 0, row:2}, name: 'ICM S2' }*/ let layout = { width: 1000, height: 600, title: 'Sensitivity Analaysis: Decomposition of Final Fraction of Infection Variance', grid: {rows: 2, columns: 2}, yaxis: { title: 'yaxis1', domain: [0, 0.30] }, annotations: [{ xref: 'paper', yref: 'paper', x: 0.001, xanchor: 'center', y: 0.85, yanchor: 'bottom', text: 'S1 ICM', showarrow: false, font: { //family: 'sans serif', size: 18, color: '#1f77b4' } },{ xref: 'paper', yref: 'paper', x: 0.001, xanchor: 'center', y: 0.15, yanchor: 'bottom', text: 'ST ICM', showarrow: false, font: { //family: 'sans serif', size: 18, color: '#ff7f0e' } },{ xref: 'paper', yref: 'paper', x: 0.95, xanchor: 'center', y: 0.80, yanchor: 'bottom', text: 'S1 LTM', showarrow: false, font: { size: 18, color: '#1f77b4' } },{ xref: 'paper', yref: 'paper', x: 0.95, xanchor: 'center', y: 0.15, yanchor: 'bottom', text: 'ST LTM', showarrow: false, font: { size: 18, color: '#ff7f0e' } }] } const div = DOM.element('div') Plotly.newPlot(div,[trace1, trace2, trace3, trace4],layout) return div }53:[{"id":"be69a00913a45a6250c6b46f38c69afd1539049e5970e4f79ad069fb02031c32170527e213aeda5ce1669a3936840846f118c89de20a87d20eea846a2d11d0cd","href":"https://static.observableusercontent.com/files/be69a00913a45a6250c6b46f38c69afd1539049e5970e4f79ad069fb02031c32170527e213aeda5ce1669a3936840846f118c89de20a87d20eea846a2d11d0cd","name":"LFTM_S1.csv","type":"text/csv","lastModified":1680275929608.721,"size":160},{"id":"52d82d007b5fbefa5585a70973d069d03208c493646711de8dc67dc306cf1adf1612a42eb2f4887e89b17808d8e986e14ad9bddc1e1f436fcf3a3d2d3efac24e","href":"https://static.observableusercontent.com/files/52d82d007b5fbefa5585a70973d069d03208c493646711de8dc67dc306cf1adf1612a42eb2f4887e89b17808d8e986e14ad9bddc1e1f436fcf3a3d2d3efac24e","name":"ICM_ST.csv","type":"text/csv","lastModified":1680289889199.249,"size":159},{"id":"4e94480a772dc76c43d7c3a426be814a959eb4927c75966a65faf38f3c3a86956ee874542517753c2b6b8c7bf63c519fafdd7a7d78f4f503bd8afac7f3f0edc1","href":"https://static.observableusercontent.com/files/4e94480a772dc76c43d7c3a426be814a959eb4927c75966a65faf38f3c3a86956ee874542517753c2b6b8c7bf63c519fafdd7a7d78f4f503bd8afac7f3f0edc1","name":"LATM_S2.csv","type":"text/csv","lastModified":1680289889165.009,"size":215},{"id":"7a7b149425f8d1390f79d187f125267e68568d3a1eb37c659f0982c92a41fb239f9cb6a2127f27d11cb3240fe465ecbc1b59978caea2a6be27aa12558bc54d9a","href":"https://static.observableusercontent.com/files/7a7b149425f8d1390f79d187f125267e68568d3a1eb37c659f0982c92a41fb239f9cb6a2127f27d11cb3240fe465ecbc1b59978caea2a6be27aa12558bc54d9a","name":"SLFTM_S2.csv","type":"text/csv","lastModified":1680289889170.075,"size":420},{"id":"aacd280040085d17a586d325f2a24a5711e4737e27879b9b2e6486fb9c0fb1eac7cff4818cb2bbe58e9b643351d9dd03f310360cac5836ac43478ac0f2885e9e","href":"https://static.observableusercontent.com/files/aacd280040085d17a586d325f2a24a5711e4737e27879b9b2e6486fb9c0fb1eac7cff4818cb2bbe58e9b643351d9dd03f310360cac5836ac43478ac0f2885e9e","name":"SLFTM_S1.csv","type":"text/csv","lastModified":1680289889196.014,"size":210},{"id":"59f52ac3fd9a4155ca1a75e2220e359c961e5ea6cd22323d55047a6c31befe3ca16d3838ad1d0a23ac4f0655ae68f458b5541a971d4f1cfcfa9d1004613bb562","href":"https://static.observableusercontent.com/files/59f52ac3fd9a4155ca1a75e2220e359c961e5ea6cd22323d55047a6c31befe3ca16d3838ad1d0a23ac4f0655ae68f458b5541a971d4f1cfcfa9d1004613bb562","name":"DWM_S2.csv","type":"text/csv","lastModified":1680289889161.201,"size":418},{"id":"9405e2faf616b2f5d4191d9bc0daa112075b116c4ac4364148405fcd30e74b2630ad7c95ae251c360c869a460c9fb2b21a3f6f6ce95e29ab395f0a08d99cf951","href":"https://static.observableusercontent.com/files/9405e2faf616b2f5d4191d9bc0daa112075b116c4ac4364148405fcd30e74b2630ad7c95ae251c360c869a460c9fb2b21a3f6f6ce95e29ab395f0a08d99cf951","name":"DWM_S1.csv","type":"text/csv","lastModified":1680289889171.098,"size":212},{"id":"82c3a4685e0f1a6d446f00d1e3a7e20913be4c649ab9e86b50b95d1dcfb096b298d62960e65e560df380a4fecd761fc6cf1d143739622c50e3e3b1a76d355815","href":"https://static.observableusercontent.com/files/82c3a4685e0f1a6d446f00d1e3a7e20913be4c649ab9e86b50b95d1dcfb096b298d62960e65e560df380a4fecd761fc6cf1d143739622c50e3e3b1a76d355815","name":"BRRM_ST.csv","type":"text/csv","lastModified":1680289889112.045,"size":163},{"id":"11448408a11fe7769c5a0112542c486d85a1d33c52da9266367b3cfb31d65c8f051df16702cb3a4d5ccbe923521062618b8ecf260d7b61f246a7b9703ced604a","href":"https://static.observableusercontent.com/files/11448408a11fe7769c5a0112542c486d85a1d33c52da9266367b3cfb31d65c8f051df16702cb3a4d5ccbe923521062618b8ecf260d7b61f246a7b9703ced604a","name":"BRRM_S2.csv","type":"text/csv","lastModified":1680289889165.251,"size":217},{"id":"aa7c9e90f5ef98aee9bb928ae0a309be95d047c92dbea3c26524ec342c831ea2978cdfaf853484adbe1215899201b2dcdb69ae4f33ee714850d9a5023b9bcea0","href":"https://static.observableusercontent.com/files/aa7c9e90f5ef98aee9bb928ae0a309be95d047c92dbea3c26524ec342c831ea2978cdfaf853484adbe1215899201b2dcdb69ae4f33ee714850d9a5023b9bcea0","name":"DWM_ST.csv","type":"text/csv","lastModified":1680289889199.352,"size":211},{"id":"4ff64ca0a5d617d3981ea54b42e32183b5a5cfc1994845c505a81ed1e7fa8adb2c1012d3cdd3f3f50f25b49dbebd4bf0b2602c95023e6a85c42e22fb43d9f15d","href":"https://static.observableusercontent.com/files/4ff64ca0a5d617d3981ea54b42e32183b5a5cfc1994845c505a81ed1e7fa8adb2c1012d3cdd3f3f50f25b49dbebd4bf0b2602c95023e6a85c42e22fb43d9f15d","name":"LATM_S1.csv","type":"text/csv","lastModified":1680289889198.46,"size":159},{"id":"e6b492fcdd9bbd557788166071e10739ccf700bc76cad1cf5b9f5cfe6787557d14f482a8e239f3c6a4469abc7be008eb68df9d343e6484ccb88479d7a9e5630f","href":"https://static.observableusercontent.com/files/e6b492fcdd9bbd557788166071e10739ccf700bc76cad1cf5b9f5cfe6787557d14f482a8e239f3c6a4469abc7be008eb68df9d343e6484ccb88479d7a9e5630f","name":"BRRM_S1.csv","type":"text/csv","lastModified":1680289889503.825,"size":163},{"id":"026ddd6edca387547118220f2a64ee3584e8ccc324d387bf7fd0a9a060dcdffe449ed0f8878e2b723677a98d9c14d2547c17107dae78bb9a8a99f88b5a33695f","href":"https://static.observableusercontent.com/files/026ddd6edca387547118220f2a64ee3584e8ccc324d387bf7fd0a9a060dcdffe449ed0f8878e2b723677a98d9c14d2547c17107dae78bb9a8a99f88b5a33695f","name":"SLFTM_ST.csv","type":"text/csv","lastModified":1680289889459.217,"size":210},{"id":"a57f0e8f97ef40ba0c89be0d29ed02d4de08fc45397cb0c824650b0803dc60fe716eeacb9368033f6c770fae2a47a2bd62887ea2fcb0d31d213c14b39b7fd11e","href":"https://static.observableusercontent.com/files/a57f0e8f97ef40ba0c89be0d29ed02d4de08fc45397cb0c824650b0803dc60fe716eeacb9368033f6c770fae2a47a2bd62887ea2fcb0d31d213c14b39b7fd11e","name":"ICM_S2.csv","type":"text/csv","lastModified":1680289889466.748,"size":214},{"id":"ed257a1c27d63553ab73680445bde2d8b0c5f84419643dc3c598fd162a320f819abd1f37b6d48020d8dcb0a8b9702de953e3d9b8e932c49976e8bb217f00a737","href":"https://static.observableusercontent.com/files/ed257a1c27d63553ab73680445bde2d8b0c5f84419643dc3c598fd162a320f819abd1f37b6d48020d8dcb0a8b9702de953e3d9b8e932c49976e8bb217f00a737","name":"LATM_ST.csv","type":"text/csv","lastModified":1680289889496.303,"size":157},{"id":"26be5ea90f2208c1a0df694bca57493db1ed82746e8e6854518c321cd4017a8da1be5da7454112e2eb3c7ec9d55e3438d92ae70383d5767b0e4455b2490fb132","href":"https://static.observableusercontent.com/files/26be5ea90f2208c1a0df694bca57493db1ed82746e8e6854518c321cd4017a8da1be5da7454112e2eb3c7ec9d55e3438d92ae70383d5767b0e4455b2490fb132","name":"ICM_S1.csv","type":"text/csv","lastModified":1680289889483.946,"size":158},{"id":"4ff64ca0a5d617d3981ea54b42e32183b5a5cfc1994845c505a81ed1e7fa8adb2c1012d3cdd3f3f50f25b49dbebd4bf0b2602c95023e6a85c42e22fb43d9f15d","href":"https://static.observableusercontent.com/files/4ff64ca0a5d617d3981ea54b42e32183b5a5cfc1994845c505a81ed1e7fa8adb2c1012d3cdd3f3f50f25b49dbebd4bf0b2602c95023e6a85c42e22fb43d9f15d","name":"LATM_S1@1.csv","type":"text/csv","lastModified":1680290022453.637,"size":159},{"id":"4e94480a772dc76c43d7c3a426be814a959eb4927c75966a65faf38f3c3a86956ee874542517753c2b6b8c7bf63c519fafdd7a7d78f4f503bd8afac7f3f0edc1","href":"https://static.observableusercontent.com/files/4e94480a772dc76c43d7c3a426be814a959eb4927c75966a65faf38f3c3a86956ee874542517753c2b6b8c7bf63c519fafdd7a7d78f4f503bd8afac7f3f0edc1","name":"LATM_S2@1.csv","type":"text/csv","lastModified":1680290022456.121,"size":215},{"id":"ed257a1c27d63553ab73680445bde2d8b0c5f84419643dc3c598fd162a320f819abd1f37b6d48020d8dcb0a8b9702de953e3d9b8e932c49976e8bb217f00a737","href":"https://static.observableusercontent.com/files/ed257a1c27d63553ab73680445bde2d8b0c5f84419643dc3c598fd162a320f819abd1f37b6d48020d8dcb0a8b9702de953e3d9b8e932c49976e8bb217f00a737","name":"LATM_ST@1.csv","type":"text/csv","lastModified":1680290022458.221,"size":157},{"id":"dcd946f44d0e3056d7d8f375f2461b0a815ab5a5a7278ac2b67dc8eafcd2cfe8882d8b118e4ad173c5b565570b64ea238e3086eb013025355f234d76b69bc690","href":"https://static.observableusercontent.com/files/dcd946f44d0e3056d7d8f375f2461b0a815ab5a5a7278ac2b67dc8eafcd2cfe8882d8b118e4ad173c5b565570b64ea238e3086eb013025355f234d76b69bc690","name":"SLATM_ST@2.csv","type":"text/csv","lastModified":1680290168268.036,"size":212},{"id":"b369743f67b2d9a467d8ab50e786e8ab7eb740d03c0d6bc810339f024cc70b8276aac565a9969a39c971126dacbaaabaad4f4b0b3daf0cf602b409327610c983","href":"https://static.observableusercontent.com/files/b369743f67b2d9a467d8ab50e786e8ab7eb740d03c0d6bc810339f024cc70b8276aac565a9969a39c971126dacbaaabaad4f4b0b3daf0cf602b409327610c983","name":"SLATM_S1@2.csv","type":"text/csv","lastModified":1680290168267.797,"size":213},{"id":"91b3277a1bc58716f8e4ea4165c8dc7e0cd0e332d9a642f37bf32ed80cb288778d99a3cb50208254eb9705be894c6af5bc42722517851491d76f309256493fd6","href":"https://static.observableusercontent.com/files/91b3277a1bc58716f8e4ea4165c8dc7e0cd0e332d9a642f37bf32ed80cb288778d99a3cb50208254eb9705be894c6af5bc42722517851491d76f309256493fd6","name":"SLATM_S2@2.csv","type":"text/csv","lastModified":1680290168267.378,"size":423}] 29:{"id":"57a8b6d8c6c5b658","slug":null,"title":"ICM vs LTM","thumbnail":"81c351a164791f2be3f0a7c60e1f05ce540ae0f016227376da6d6dc9af3800f5","access_level":"public","create_time":"$D2023-03-31T14:53:31.858Z","update_time":"$D2023-03-31T19:12:55.170Z","view_time":null,"first_public_time":"$D2023-03-31T14:53:31.858Z","has_unpublished_edits":null,"fork":{"id":"dee855ff648109ac","slug":"icm-vs-ltm","title":"ICM vs LTM","owner":{"id":"faa67bfd1359f951","login":"deamonpog","name":"Chathura Jayalath"}},"stars":0,"starred":false,"owner":{"type":"team","id":"faa67bfd1359f951","login":"deamonpog","name":"Chathura Jayalath","avatar_url":"https://avatars-next.observableusercontent.com/1bc13a0724d43e94c26de301357a2897d22e5c4e01e7f160ea8fba232ef861f1","tier":"starter_2024","bio":"","followed_by_you":false,"follows_you":false},"version":1766,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"5fe81dea2f61079a","login":"deamonpog","name":"Chathura Jayalath","avatar_url":"https://avatars-next.observableusercontent.com/1bc13a0724d43e94c26de301357a2897d22e5c4e01e7f160ea8fba232ef861f1","tier":null,"bio":"","followed_by_you":false,"follows_you":false}],"members":[{"type":"individual","id":"5fe81dea2f61079a","login":"deamonpog","name":"Chathura Jayalath","avatar_url":"https://avatars-next.observableusercontent.com/1bc13a0724d43e94c26de301357a2897d22e5c4e01e7f160ea8fba232ef861f1","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"titling":"auto","title":"ICM vs LTM","cells":[{"id":0,"value":"md`# ICM vs LTM`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1581,"value":"slatm_s1 = FileAttachment(\"SLATM_S1.csv\").csv({typed:true})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1615,"value":"slatm_s2 = FileAttachment(\"SLATM_S2.csv\").csv({typed:true})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1617,"value":"slatm_st = FileAttachment(\"SLATM_ST.csv\").csv({typed:true})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1623,"value":"lftm_s1 = FileAttachment(\"LFTM_S1@2.csv\").csv({typed:true})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1625,"value":"lftm_s2 = FileAttachment(\"LFTM_S2.csv\").csv({typed:true})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1631,"value":"lftm_st = FileAttachment(\"LFTM_ST.csv\").csv({typed:true})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1764,"value":"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1587,"value":"data = ({\"SLATM\" : {\"S1\":slatm_s1, \"S2\":slatm_s2, \"ST\":slatm_st}, \"LFTM\": {\"S1\":lftm_s1, \"S2\":lftm_s2, \"ST\":lftm_st} })","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1591,"value":"getAttributeData( data[\"SLATM\"][\"S1\"], \"\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1741,"value":"Array(4).fill(0).map((x,i) => i)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1731,"value":"var_to_symb = ({'init_infection':'\\u03D5\\u2080','beta':'\\u03B2',11:'q_b',4:'q',7:'\\u03B8','SLATM_t':'\\u03C8'})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1639,"value":"$49","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1635,"value":"$4a","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":188,"value":"md`## Plots`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1371,"value":"$4b","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1401,"value":"$4c","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1148,"value":"$4d","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":871,"value":"md`### Total Sensitivities`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1067,"value":"{\n let trace1 = {\n values: RRM_ST,\n labels: ['\\u03D5\\u2080','\\u03B2','q_b'], //['Initial-Infects', 'Beta', 'BRRM-Q'],\n textinfo: 'label+percent',\n textposition: 'inside',\n type: 'pie',\n name: \"BRRM\",\n //marker: { colors: [pdefColors[0],pdefColors[1],rstudioColors[0]] }\n marker: { colors: [pdefColors[0],pdefColors[1],pdefColors[3]] }\n }\n\n let layout = {\n //title: 'ST BRRM' ,\n height: 500,\n width: 500,\n font: {\n size: 35,\n },\n showlegend: false\n }\n const div = DOM.element('div')\n Plotly.newPlot(div,[trace1],layout)\n return div\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":840,"value":"{\n let trace1 = {\n values: ICM_ST,\n labels: ['\\u03D5\\u2080','\\u03B2','q'], //['Initial-Infects', 'Beta', 'ICM-Q'],\n textinfo: 'label+percent',\n textposition: 'inside',\n type: 'pie',\n name: \"LATM\",\n marker: { colors: [pdefColors[0],pdefColors[1],pdefColors[2]] }\n }\n\n let layout = {\n //title: 'Total Sensitivities' ,\n height: 500,\n width: 500,\n font: {\n size: 35,\n },\n showlegend: false\n }\n const div = DOM.element('div')\n Plotly.newPlot(div,[trace1],layout)\n return div\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":859,"value":"{\n let trace3 = {\n values: LFTM_ST,\n labels: ['\\u03D5\\u2080','\\u03B2','\\u03B8'],// ['Initial-Infects', 'Beta', 'LFTM-Threshold'],\n textinfo: 'label+percent',\n textposition: 'inside',\n type: 'pie',\n name: \"LFTM\",\n marker: { colors: [pdefColors[0],pdefColors[1],pdefColors[8]] }\n }\n let layout = {\n //title: 'Total Sensitivities' ,\n height: 500,\n width: 500,\n font: {\n size: 35,\n },\n showlegend: false\n }\n const div = DOM.element('div')\n Plotly.newPlot(div,[trace3],layout)\n return div\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":855,"value":"{\n let trace2 = {\n values: LTM_ST,\n labels: ['\\u03D5\\u2080','\\u03B2','\\u03C8'], //['Initial-Infects', 'Beta', 'LATM-Threshold'],\n textinfo: 'label+percent',\n textposition: 'inside',\n type: 'pie',\n name: \"ICM\",\n marker: { colors: [pdefColors[0],pdefColors[1],pdefColors[4]] }\n }\n\n let layout = {\n //title: 'Total Sensitivities' ,\n height: 500,\n width: 500,\n font: {\n size: 35,\n },\n showlegend: false\n }\n const div = DOM.element('div')\n Plotly.newPlot(div,[trace2],layout)\n return div\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1449,"value":"## Old Plots","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":542,"value":"$4e","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":874,"value":"md`### 1st Order Sensitivities`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":877,"value":"{\n let trace1 = {\n values: ICM_S1,\n labels: ['Initial-Infects', 'Beta', 'ICM-Q','ICM-Interactions'],\n textinfo: 'label+percent',\n textposition: 'outside',\n type: 'pie',\n name: \"LATM\",\n marker: { colors: [pdefColors[0],pdefColors[1],pdefColors[2],pdefColors[4]] }\n }\n\n let layout = {\n //title: '1st Order Sensitivities' ,\n height: 600,\n width: 500,\n font: {\n size: 20,\n },\n showlegend: false\n }\n const div = DOM.element('div')\n Plotly.newPlot(div,[trace1],layout)\n return div\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":888,"value":"{\n let trace1 = {\n values: LFTM_S1,\n labels: ['Initial-Infects', 'Beta', 'LFTM-Threshold','LFTM-Interactions'],\n textinfo: 'label+percent',\n textposition: 'outside',\n type: 'pie',\n name: \"LATM\",\n marker: { colors: [pdefColors[0],pdefColors[1],pdefColors[4],pdefColors[6]] }\n }\n\n let layout = {\n //title: '1st Order Sensitivities' ,\n height: 600,\n width: 500,\n font: {\n size: 20,\n },\n showlegend: false\n }\n const div = DOM.element('div')\n Plotly.newPlot(div,[trace1],layout)\n return div\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":896,"value":"{\n let trace1 = {\n values: LTM_S1,\n labels: ['Initial-Infects', 'Beta', 'LATM-Threshold','LATM-Interactions'],\n textinfo: 'label+percent',\n textposition: 'outside',\n type: 'pie',\n name: \"LATM\",\n marker: { colors: [pdefColors[0],pdefColors[1],pdefColors[3],pdefColors[5]] }\n }\n\n let layout = {\n //title: '1st Order Sensitivities' ,\n height: 600,\n width: 500,\n showlegend: false,\n font: {\n size: 20,\n }\n }\n const div = DOM.element('div')\n Plotly.newPlot(div,[trace1],layout)\n return div\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1075,"value":"{\n let trace1 = {\n values: RRM_S1,\n labels: ['Initial-Infects', 'Beta', 'BRRM-Q','BRRM-Interactions'],\n textinfo: 'label+percent',\n textposition: 'outside',\n type: 'pie',\n name: \"RRM\",\n marker: { colors: [pdefColors[0],pdefColors[1],pdefColors[3],pdefColors[8]] }\n }\n\n let layout = {\n //title: '1st Order Sensitivities' ,\n height: 600,\n width: 500,\n showlegend: false,\n font: {\n size: 20,\n }\n }\n const div = DOM.element('div')\n Plotly.newPlot(div,[trace1],layout)\n return div\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":982,"value":"md`## 2nd Order Interactions`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":986,"value":"{\n let data = ICM_S2.map(x => Math.abs(x));\n data[3] = 1 - data[0] - data[1] - data[2];\n let trace1 = {\n values: data,\n labels: ICM_S2_names.concat(['Other']),\n textinfo: 'label+percent',\n //textposition: 'outside',\n type: 'pie',\n name: \"ICM\",\n sort: false,\n marker: { colors: [pdefColors[0],pdefColors[2],pdefColors[3],pdefColors[7]] }\n }\n\n let layout = {\n //title: '1st Order Sensitivities' ,\n height: 500,\n width: 1000,\n margin: {\n l: 100,\n r: 50,\n b: 100,\n t: 100,\n pad: 4\n },\n showlegend: true,\n font: {\n size: 20,\n }\n }\n const div = DOM.element('div')\n Plotly.newPlot(div,[trace1],layout)\n return div\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1009,"value":"{\n let data = LTM_S2.map(x=> Math.abs(x));\n data[3] = 1 - data[0] - data[1] - data[2];\n let trace1 = {\n values: data,\n labels: LTM_S2_names.concat(['Other']),\n textinfo: 'label+percent',\n //textposition: 'outside',\n type: 'pie',\n name: \"LATM\",\n sort: false,\n marker: { colors: [pdefColors[0],pdefColors[2],pdefColors[3],pdefColors[7]] }\n }\n\n let layout = {\n //title: '1st Order Sensitivities' ,\n height: 500,\n width: 1000,\n margin: {\n l: 100,\n r: 50,\n b: 100,\n t: 100,\n pad: 4\n },\n showlegend: true,\n font: {\n size: 20,\n }\n }\n const div = DOM.element('div')\n Plotly.newPlot(div,[trace1],layout)\n return div\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1025,"value":"{\n let data = LFTM_S2.map(x=> Math.abs(x));\n data[3] = 1 - data[0] - data[1] - data[2];\n let trace1 = {\n values: data,\n labels: LFTM_S2_names.concat(['Other']),\n textinfo: 'label+percent',\n //textposition: 'outside',\n type: 'pie',\n name: \"LFTM\",\n sort: false,\n marker: { colors: [pdefColors[0],pdefColors[2],pdefColors[3],pdefColors[7]] }\n }\n\n let layout = {\n //title: '1st Order Sensitivities' ,\n height: 500,\n width: 1000,\n margin: {\n l: 100,\n r: 50,\n b: 100,\n t: 100,\n pad: 4\n },\n showlegend: true,\n font: {\n size: 20,\n }\n }\n const div = DOM.element('div')\n Plotly.newPlot(div,[trace1],layout)\n return div\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1081,"value":"{\n let data = RRM_S2.map(x=> Math.abs(x));\n data[3] = 1 - data[0] - data[1] - data[2];\n let trace1 = {\n values: data,\n labels: RRM_S2_names.concat(['Other']),\n textinfo: 'label+percent',\n //textposition: 'outside',\n type: 'pie',\n name: \"RRM\",\n sort: false,\n marker: { colors: [pdefColors[0],pdefColors[2],pdefColors[3],pdefColors[7]] }\n }\n\n let layout = {\n //title: '1st Order Sensitivities' ,\n height: 500,\n width: 1000,\n margin: {\n l: 100,\n r: 50,\n b: 100,\n t: 100,\n pad: 4\n },\n showlegend: true,\n font: {\n size: 20,\n }\n }\n const div = DOM.element('div')\n Plotly.newPlot(div,[trace1],layout)\n return div\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":833,"value":"md`## Data Preparation`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1349,"value":"rstudioColors = [\"#F8766D\",\"#7CAE00\",\"#00BFC4\",\"#C77CFF\", \"#D89000\", \"#A3A500\", \"#39B600\", \"#00BF7D\", \"#00B0F6\", \"#9590FF\", \"#E76BF3\", \"#FF62BC\"]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":844,"value":"pdefColors = [ '#1f77b4', // muted blue\n '#ff7f0e', // safety orange\n '#2ca02c', // cooked asparagus green\n '#d62728', // brick red\n '#9467bd', // muted purple\n '#8c564b', // chestnut brown\n '#e377c2', // raspberry yogurt pink\n '#7f7f7f', // middle gray\n '#bcbd22', // curry yellow-green\n '#17becf' // blue-teal\n]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":744,"value":"$4f","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1048,"value":"RRM_S1 = { \n let data = [allData.RRM['initial-infects'][0], allData.RRM['beta'][0], allData.RRM['rrabm-q'][0]];\n data[3] = 1 - (data[0] + data[1] + data[2]);\n return data;\n }","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1050,"value":"RRM_S1_conf = [allData.RRM['initial-infects'][1], allData.RRM['beta'][1], allData.RRM['rrabm-q'][1]]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1053,"value":"RRM_ST = [allData.RRM['initial-infects'][2], allData.RRM['beta'][2], allData.RRM['rrabm-q'][2]]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1055,"value":"RRM_ST_conf = [allData.RRM['initial-infects'][3], allData.RRM['beta'][3], allData.RRM['rrabm-q'][3]]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1057,"value":"RRM_S2_names = [allData.RRM['S2_Values'][0][0] + ' ' + allData.RRM['S2_Values'][0][1] ,\n allData.RRM['S2_Values'][1][0] + ' ' + allData.RRM['S2_Values'][1][1], \n allData.RRM['S2_Values'][2][0] + ' ' + allData.RRM['S2_Values'][2][1]]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1061,"value":"RRM_S2 = [allData.RRM['S2_Values'][0][2], allData.RRM['S2_Values'][1][2], allData.RRM['S2_Values'][2][2]]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1063,"value":"RRM_S2_conf = [allData.RRM['S2_Values'][0][3], allData.RRM['S2_Values'][1][3], allData.RRM['S2_Values'][2][3]]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":170,"value":"ICM_S1 = { let data = [allData.ICM['initial-infects'][0], allData.ICM['beta'][0], allData.ICM['icm-q'][0]]; \n \n data[3] = 1 - (data[0] + data[1] + data[2]); \n return data }","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":320,"value":"ICM_S1_conf = [allData.ICM['initial-infects'][1], allData.ICM['beta'][1], allData.ICM['icm-q'][1]]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":172,"value":"ICM_ST = [allData.ICM['initial-infects'][2], allData.ICM['beta'][2], allData.ICM['icm-q'][2]]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":516,"value":"ICM_ST_conf = [allData.ICM['initial-infects'][3], allData.ICM['beta'][3], allData.ICM['icm-q'][3]]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":958,"value":"ICM_S2_names = [allData.ICM['S2_Values'][0][0] + ' ' + allData.ICM['S2_Values'][0][1] ,\n allData.ICM['S2_Values'][1][0] + ' ' + allData.ICM['S2_Values'][1][1], \n allData.ICM['S2_Values'][2][0] + ' ' + allData.ICM['S2_Values'][2][1]]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":942,"value":"ICM_S2 = [allData.ICM['S2_Values'][0][2], allData.ICM['S2_Values'][1][2], allData.ICM['S2_Values'][2][2]]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":955,"value":"ICM_S2_conf = [allData.ICM['S2_Values'][0][3], allData.ICM['S2_Values'][1][3], allData.ICM['S2_Values'][2][3]]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":176,"value":"LTM_S1 = { let data = [allData.LATM['initial-infects'][0], allData.LATM['beta'][0], allData.LATM['ltm-threshold'][0]]; \n data[3] = 1 - (data[0] + data[1] + data[2]); \n return data }","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":662,"value":"LTM_S1_conf = [allData.LATM['initial-infects'][1], allData.LATM['beta'][1], allData.LATM['ltm-threshold'][1]]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":186,"value":"LTM_ST = [allData.LATM['initial-infects'][2], allData.LATM['beta'][2], allData.LATM['ltm-threshold'][2]]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":664,"value":"LTM_ST_conf = [allData.LATM['initial-infects'][3], allData.LATM['beta'][3], allData.LATM['ltm-threshold'][3]]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":972,"value":"LTM_S2_names = [allData.LATM['S2_Values'][0][0] + ' ' + allData.LATM['S2_Values'][0][1] ,\n allData.LATM['S2_Values'][1][0] + ' ' + allData.LATM['S2_Values'][1][1], \n allData.LATM['S2_Values'][2][0] + ' ' + allData.LATM['S2_Values'][2][1]]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":974,"value":"LTM_S2 = [allData.LATM['S2_Values'][0][2], allData.LATM['S2_Values'][1][2], allData.LATM['S2_Values'][2][2]]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":969,"value":"LTM_S2_conf = [allData.LATM['S2_Values'][0][3], allData.LATM['S2_Values'][1][3], allData.LATM['S2_Values'][2][3]]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":210,"value":"LFTM_S1 = { let data = [allData.LFTM['initial-infects'][0], allData.LFTM['beta'][0], allData.LFTM['ltm-rel-threshold'][0]]; \n data[3] = 1 - (data[0] + data[1] + data[2]); return data }","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":324,"value":"LFTM_S1_conf = [allData.LFTM['initial-infects'][1], allData.LFTM['beta'][1], allData.LFTM['ltm-rel-threshold'][1]]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":213,"value":"LFTM_ST = [allData.LFTM['initial-infects'][2], allData.LFTM['beta'][2], allData.LFTM['ltm-rel-threshold'][2]]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":519,"value":"LFTM_ST_conf = [allData.LFTM['initial-infects'][3], allData.LFTM['beta'][3], allData.LFTM['ltm-rel-threshold'][3]]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":976,"value":"LFTM_S2_names = [allData.LFTM['S2_Values'][0][0] + ' ' + allData.LFTM['S2_Values'][0][1] ,\n allData.LFTM['S2_Values'][1][0] + ' ' + allData.LFTM['S2_Values'][1][1], \n allData.LFTM['S2_Values'][2][0] + ' ' + allData.LFTM['S2_Values'][2][1]]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":978,"value":"LFTM_S2 = [allData.LFTM['S2_Values'][0][2], allData.LFTM['S2_Values'][1][2], allData.LFTM['S2_Values'][2][2]]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":980,"value":"LFTM_S2_conf = [allData.LFTM['S2_Values'][0][3], allData.LFTM['S2_Values'][1][3], allData.LFTM['S2_Values'][2][3]]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":515,"value":"$50","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":306,"value":"$51","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":167,"value":"$52","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":133,"value":"md`## ICM`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":136,"value":"{\n // ICM S1\n let data = [0.167831, 0.160415, 0.173729, 0]\n data[3] = 1 - (data[0] + data[1] + data[2])\n let trace1 = {\n values: data,\n labels: ['Initial-Infects', 'Beta', 'Q','Interactions'],\n textinfo: 'label+percent',\n textposition: 'outside',\n type: 'pie'\n }\n \n let layout = { title: 'ICM S1 (1st order Sensitivities)' }\n \n const div = DOM.element('div')\n Plotly.newPlot(div,[trace1],layout)\n return div\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":614,"value":"{\n // ICM S2\n let trace1 = {\n y: ICM_S2.map(x => x * 100),\n x: ['Initial-Infects * Beta', 'Initial-Infects * Q', 'Beta * Q'],\n type: 'scatter',\n mode:'markers',\n error_y :{ array: ICM_S2_conf.map(x=>x*100) }\n }\n \n let layout = { title: 'ICM S2 (2nd order Sensitivities)' }\n \n const div = DOM.element('div')\n Plotly.newPlot(div,[trace1],layout)\n return div\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":141,"value":"{\n // LTM TOTAL SENSITIVITIES\n let data = [0.96622775, 0.86476299, 0.96543949]\n let trace1 = {\n values: data,\n labels: ['Initial-Infects', 'Beta', 'Threshold'],\n type: 'pie'\n }\n let layout = { title: 'ICM Total Sensitivities' }\n const div = DOM.element('div')\n Plotly.newPlot(div,[trace1],layout)\n return div\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":130,"value":"md`## LTM`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":110,"value":"{\n // LTM S1\n let data = [0.094770, 0.067111, 0.047767, 0]\n data[3] = 1 - (data[0] + data[1] + data[2])\n let trace1 = {\n values: data,\n labels: ['Initial-Infects', 'Beta', 'LATM-Threshold','LATM-Interactions'],\n textinfo: 'label+percent',\n textposition: 'outside',\n type: 'pie',\n marker: {\n colors: ['#2ca02c','#d62728', '#9467bd', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf']\n }\n }\n \n let layout = { title: 'LTM S1 (1st order Sensitivities)' }\n \n const div = DOM.element('div')\n Plotly.newPlot(div,[trace1],layout)\n return div\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":117,"value":"{\n // LTM TOTAL SENSITIVITIES\n let data = [0.90655313, 0.85807288, 0.94846557]\n let trace1 = {\n values: data,\n labels: ['Initial-Infects', 'Beta', 'LATM-Threshold'],\n textinfo: 'label+percent',\n textposition: 'outside',\n type: 'pie',\n sort: false,\n marker: {\n colors: ['#2ca02c','#d62728', '#9467bd']\n }\n }\n let layout = { title: 'LTM Total Sensitivities' }\n const div = DOM.element('div')\n Plotly.newPlot(div,[trace1],layout)\n return div\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":704,"value":"{\n // LTM TOTAL SENSITIVITIES\n let data = [0.90655313, 0.85807288, 0.94846557]\n let trace1 = {\n values: ICM_ST,\n labels: ['Initial-Infects', 'Beta', 'ICM-Q'],\n textinfo: 'label+percent',\n textposition: 'outside',\n domain: {column: 0, row:0},\n type: 'pie',\n name: \"LATM\"\n }\n // ICM\n let trace2 = {\n values: LTM_ST,\n labels: ['Initial-Infects', 'Beta', 'LATM-Threshold'],\n textinfo: 'label+percent',\n textposition: 'outside',\n domain: {column: 1, row:1},\n type: 'pie',\n name: \"ICM\"\n }\n // LFTM\n let trace3 = {\n values: LFTM_ST,\n labels: ['Initial-Infects', 'Beta', 'LFTM-Threshold'],\n textinfo: 'label+percent',\n textposition: 'outside',\n domain: {column: 2, row:2},\n type: 'pie',\n name: \"LFTM\"\n }\n let layout = {\n title: 'LTM Total Sensitivities' ,\n height: 500,\n width: 1000,\n grid: {rows: 3, columns: 3},\n showlegend: false\n }\n const div = DOM.element('div')\n Plotly.newPlot(div,[trace1, trace2, trace3],layout)\n return div\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":145,"value":"md`## LTM Fractional Threshold`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":150,"value":"{\n // LTMFractional S1\n let data = [0.08671841, 0.06521653, 0.03673105, 0]\n data[3] = 1 - (data[0] + data[1] + data[2])\n let trace1 = {\n values: data,\n labels: ['Initial-Infects', 'Beta', 'FractionalThreshold','Interactions'],\n type: 'pie'\n }\n \n \n let layout = { \n title: 'LTMFractional S1 (1st order Sensitivities)',\n //grid: {rows: 2, columns: 1}\n }\n \n const div = DOM.element('div')\n Plotly.newPlot(div,[trace1],layout)\n return div\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":156,"value":"{\n // LTMFractional TOTAL SENSITIVITIES\n let data = [1.02254216, 0.90952864, 0.93467876]\n let trace1 = {\n values: data,\n labels: ['Initial-Infects', 'Beta', 'FractionalThreshold'],\n type: 'pie'\n }\n let layout = { title: 'LTMFractional Total Sensitivities' }\n const div = DOM.element('div')\n Plotly.newPlot(div,[trace1],layout)\n return div\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":161,"value":"{\n // LTMFractional S2\n let data = [0.09918726, 0.06294382, 0.07990901, 0]\n data[3] = 1 - (data[0] + data[1] + data[2])\n let trace1 = {\n values: data,\n labels: ['Initial-Infects * Beta', 'Initial-Infects * FractionalThreshold', 'Beta * FractionalThreshold','Interactions'],\n type: 'pie'\n }\n \n let layout = { title: 'LTMFractional S2 (2nd order Sensitivities)' }\n \n const div = DOM.element('div')\n Plotly.newPlot(div,[trace1],layout)\n return div\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":23,"value":"{\n let trace1 = {\n x: getAttributeData(ICMData, 'initialInfects'),\n y: getAttributeData(ICMData, 'finalInfection'),\n type: 'scatter',\n mode: 'markers'\n }\n \n const div = DOM.element('div')\n Plotly.newPlot(div,[trace1])\n return div\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":17,"value":"ICMData.columns","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":19,"value":"LTMData.columns","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":48,"value":"/*{\n let data = generateKernalDensity(iuedges, 0.006, 1, kernalEpanechnikovFactory());\n let trace2 = {\n x: data[0],\n y: data[1],\n type: 'scatter',\n name: 'KDE'\n };\n const div = DOM.element('div');\n Plotly.newPlot(div, [trace2])\n return div\n \n}*/","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":60,"value":"iuedges = getAttributeData(ICMData,'calculateIU')","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":104,"value":"getFilteredData(ICMData, 'model', 'ICM')","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":54,"value":"md`## Helpers`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":681,"value":"defcolors = [\n '#1f77b4', // muted blue\n '#ff7f0e', // safety orange\n '#2ca02c', // cooked asparagus green\n '#d62728', // brick red\n '#9467bd', // muted purple\n '#8c564b', // chestnut brown\n '#e377c2', // raspberry yogurt pink\n '#7f7f7f', // middle gray\n '#bcbd22', // curry yellow-green\n '#17becf' // blue-teal\n ]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":57,"value":"getAttributeData = (dataSource,attribute)=>dataSource.map(d=>d[attribute])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":102,"value":"getFilteredData = (dataSource, attribute, value) => dataSource.filter(function(d){ return d[attribute] == value })","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":45,"value":"md`## Libraries`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":5,"value":"d3 = require(\"d3@5\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":26,"value":"Plotly = require(\"https://cdn.plot.ly/plotly-latest.min.js\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":40,"value":"import {generateKDEPlot,generateKernalDensity,kernalEpanechnikovFactory} from '@deamonpog/cap-5738-assignment-2-chathura-jayalath-ucfid-3948890'","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1558,"value":"{\n let trace_ICM = {\n y: getAttributeData(ICMData, 'finalInfection'),\n type: 'violin',\n points: 'none',\n box: { visible: true},\n meanline: { visible: true },\n x0: \"ICM\"\n }\n\n let trace_LFTM = {\n y: getAttributeData(LTMData, 'finalInfection'),\n type: 'violin',\n points: 'none',\n box: { visible: true},\n meanline: { visible: true },\n x0: \"LFTM\"\n }\n\n let trace_LATM = {\n y: getAttributeData(LTMData, 'finalInfection'),\n type: 'violin',\n points: 'none',\n box: { visible: true},\n meanline: { visible: true },\n x0: \"LATM\"\n }\n\n let layout = {\n title: \"Distribution of Final Fraction of Infection\",\n yaxis: { zeroline: false }\n }\n \n const div = DOM.element('div')\n Plotly.newPlot(div,[trace_ICM, trace_LFTM, trace_LATM], layout)\n return div\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":42,"value":"md`## Data`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2,"value":"ICMData = d3.csv(\"https://gist.githubusercontent.com/deamonpog/1d28c1f96f3af49fa2b013913914652c/raw/4112c4d11fd68cbe789d131fc939e93ff07108c8/experiment_ICM_VariedBeta\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":9,"value":"LTMData = d3.csv(\"https://gist.githubusercontent.com/deamonpog/1d28c1f96f3af49fa2b013913914652c/raw/4112c4d11fd68cbe789d131fc939e93ff07108c8/experiment_LTM_VariedBeta\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":37,"value":"AllData = d3.csv('https://gist.githubusercontent.com/deamonpog/f08f4b0b9b6a29b2268ebd1766999786/raw/5a41b9a8d0ddf34e3f188793bef4aab8d2a2cf64/gistfile1.txt')","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W53","stdlib":"1"}} 24:null